Hyperspectral Image Restoration Based On Transformation Domain

Hyperspectral images(HSI)are inevitably contaminated by mixed noise during the acquisition process,such as Gaussian noise,impulse noise,stripes,deadlines and many others,which seriously affects the image quality.To improve the image quality,HSI denoising is a critical preprocessing step.To improve the accuracy and efficiency of HSI denoising,two new HSI denoising models are proposed,and an effective solution algorithm is designed,which is proved the effectiveness through multiple experiments.Specifically,the main work is as follows:(1)In this thesis,a hyperspectral image denoising model via framelet transformation based three-modal tensor nuclear norm is proposed.Among numerous denoising methods based on low-rank prior,the tensor nuclear norm(TNN),based on the tensor singular value decomposition(t-SVD),is employed to describe the low-rank prior approximately.Its calculation can be accelerated by the fast Fourier transform(FFT).However,TNN is computed by the Fourier transform,which lacks the function of locating frequency.Besides,it only describes the low-rankness of the spectral correlations and ignores the spatial dimensions' information.In this thesis,to overcome the above deficiencies,we use the basis redundancy of the framelet and the low-rank characteristics of HSI in three modes.We propose the framelet-based tensor fibered rank as a new representation of the tensor rank,and the framelet-based three-modal tensor nuclear norm(F-3MTNN)as its convex relaxation.Meanwhile,the F-3MTNN is the new regularization of the denoising model.It can explore the low-rank characteristics of HSI along three modes that are more flexible and comprehensive.Moreover,we design an efficient algorithm via the alternating direction method of multipliers(ADMM).Finally,the numerical results of several experiments have shown the superior denoising performance of the proposed F-3MTNN model.(2)Furthermore,in this thesis,a spatial non-local and local rank-constrained low-rank regularized Plugand-Play(NLRPn P)model is presented for mixed noise removal in HSIs.Specifically,we first divide HSIs into local overlapping patches.Local rank-constrained lowrank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise and a part of Gaussian noise,and to significantly preserve local structure and detail information in HSIs.Then the spatial non-local based denoiser is introduced to promote the non-local self-similarity and obviously depress the Gaussian noise.Without increasing the difficulty of solving optimization problems,we combine the local and non-local based methods into the Plug-and-Play framework,and develop an efficient algorithm for solving the proposed NLRPn P model by using the alternating direction method of multipliers method.Finally,several experiments are conducted in both simulated and real data conditions to illustrate the satisfying performance of the proposed NLRPn P model.